Data Set Overview The Mars Express (MEX) Planetary Fourier Spectrometer (PFS) Data Archive is a collection of raw data collected during the MEX Mission to Mars. For more information on the investigations proposed see the PFS documentations in the DOCUMENT/ folder. This data set was collected during the MEX Mission phases: First Extension Mission Phase Mission Phase Definition It should be noted that the Mars Express (MEX) Planetary Fourier Spectrometer (PFS) group uses mission phases which deviate from the ones defined in the MISSION.CAT files given by ESA in order to keep the keywords and abbreviations consistent for Mars Express, Venus Express and Rosetta. Those mission phase abbreviations are also used in the data description field of the dataset_id. MaRS mission name | abbreviation | time span Near Earth Verification | NEV | 20030602 20030731 Interplanetary Cruise | IC | 20030801 20031225 Nominal Mission | Nominal | 20031226 20051130 First Extension Mission | EXT1 | 20060101 20070930 Second Extension Mission| EXT2 | 20071001 20091231 Data files Data files are: The tracking files from Deep Space Network (DSN) and from the Intermediate Frequency Modulation System (IFMS) used by the ESA ground station New Norcia. Level 1b data are archived. The Geometry files All Level binary data files will have the file name extension eee .DAT Data levels It should be noted that these data levels which are also used in the file names and data directories are PSA dat truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

Refer to the current geographies boundaries table for a list of all current geographies and recent updates. This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2025 as defined by Stats NZ, clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This version contains 873 SA3s, excluding 4 non-digitised SA3s. The SA3 geography aims to meet three purposes: approximate suburbs in major, large, and medium urban areas, in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities, minimise data suppression. SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations. Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s. Zero or nominal population SA3s To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations. Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority. Small population island SA2s are included in their adjacent land-based SA3. Island SA2s outside territorial authority or region are the same in the SA3 geography. Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification. Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority. Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council. The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name): 70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region. SA3 numbering and naming Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb, recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District). SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2025, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained. ​ Clipped Version This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. ​ High-definition version This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. ​ Digital data Digital boundary data became freely available on 1 July 2007. ​ Further information To download geographic classifications in table formats such as CSV please use Ariā For more information please refer to the Statistical standard for geographic areas 2023. Contact: geography@stats.govt.nz

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnoses (CAD) systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead.

This data set can be used to predict the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient's age. It contains a BI-RADS assessment, the patient's age and three BI-RADS attributes together with the ground truth (the severity field).

Attribute Information: 1. BI-RADS assessment: 1 to 5 (ordinal, non-predictive!) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal, goal field!)

Evaluation Task: Download the dataset from attached file and perform the following tasks: 1. Build Statistical Classification model to detect severity 2. What considerations have been used for model selection? 3. What features would you want to create for your prediction model based on data provided? 4. How have you performed hyper-parameter tuning and model optimization? What are the reasons for your decision choices for these steps? 5. What is your model evaluation criteria? What are the assumptions and limitations of your approach? 6. Determine whether the data is normally distributed visually and statistically. 7. Comment on EDA of variables in data. 8. How are you detecting and treating outliers in the dataset for better convergence? 9. What techniques have been used for treating missing values to prepare features for model building? 10. What is the distribution of target with respect to categorical columns? 11. Comment on any other observations or recommendations based on your analysis.

This dataset contains multiple variables with spatio-temporal information relating to sea-ice and the southern ocean. This collection of data is utilised by the nilas.org platform for dynamically visualising these variables in the web browser. Together they provide a valuable resource for understanding the interactions between physical, climate and biogeochemical parameters. These include variables to understand sea-ice in three dimensions, chlorophyll and sea surface temperature. The time range of these data covers from 1980 until the present and the spatial coverage is Antarctic circumpolar.

Name: Daily Sea Ice Concentration Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September. Product: ARTIST (ASI 5) (Spreen et al. 2008) Source: Universität Bremen Resolution: 6.125 km nominal Timeframe: 2012 to present Notes: Concentrations of less than 15% have been removed.

Name: Monthly Sea Ice Concentration Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September. Product: Sea Ice Index (Windnagel et al. 2017) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25 km nominal Timeframe: 1980 to present Notes: Concentrations of less than 15% have been removed.

Name: Monthly Anomalies in Sea Ice Concentration Desc: Anomalies in sea ice concentration show the monthly variation from the long term mean. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to present Notes: Anomalies are calculated as the difference between the sea ice concentration and the 1981-2010 mean sea ice concentration for that month. Anomalies less than 7.5% are not shown.

Name: Long term monthly mean sea ice extent Desc: Sea ice extent is calculated as contour lines at 15% and 80% sea ice concentration. Product: Sea Ice Index (Windnagel et al. 2017) Source: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Resolution: - Timeframe: 1980 to present Notes: Contours with less than 15 vertices are discarded.

Name: Long Term Monthly Mean Sea Ice Extent Desc: Mean monthly sea ice extent over the 1981-2010 time interval. This is calculated as contour lines at 15% and 80% long term mean (1981-2010) sea ice concentration. Product: Sea Ice Index (Windnagel et al. 2017) Source: NSIDC (National Snow and Ice Data Center) Resolution: - Timeframe: Long term monthly mean (1981-2010) Notes: Contours with less than 15 vertices are discarded.

Name: Gridded Freeboard (ATL20) IceSat2 Desc: Sea ice freeboard is the distance between the waterline and the surface height of sea ice in open leads. This dataset contains monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10,V3). Product: ATL20 (Petty et al. 2020) Source: NSIDC Resolution: 25 km nominal Timeframe: Oct 2018 to July 2022 Notes: Data greater than 1 metre is shown as 1 metre height.

Name: Annual Sea Ice Duration Desc: Sea ice duration (contour lines) is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima (assumed to occur on Feb 16 each year). Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes:

Name: Sea Ice Duration Anomalies Desc: Anomalies in sea ice duration show difference in duration of sea ice from the long term mean, where sea ice duration is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes: Anomalies in sea ice duration are calculated relative to the 1981 to 2010 mean.

Name: Annual Sea Ice Advance Desc: Sea ice advance is the date when sea ice concentrations persist above 15% after the sea ice minimum. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2022 Notes:

Name: Sea Ice Advance Anomalies Desc: Anomalies in sea ice advance show number of days (early/late) from the long term mean, where sea ice advance is the date when sea ice concentrations persist above 15% after the sea ice minimum. Product: Climate Data Record an...

The Marine Environment Classification (MEC), a GIS-based environmental classification of the marine environment of the New Zealand region, is an ecosystem-based spatial framework designed for marine management purposes. Developed by NIWA with support from the Ministry for the Environment (MfE), Department of Conservation and Ministry of Fisheries, and with contributions from several other stakeholders, the MEC provides a spatial framework for inventories of marine resources, environmental effects assessments, policy development and design of protected area networks. Two levels of spatial resolution are available within the MEC. A broad scale classification covers the entire EEZ at a nominal spatial resolution of 1 km, whereas the finer scale classification of the Hauraki Gulf region has a nominal spatial resolution of 200 m. Several spatially-explicit data layers describing the physical environment define the MEC. A physically-based classification was chosen because data on these physical variables were available or could be modelled, and because the pattern of the physical environment is a reasonable surrogate for biological pattern, particularly at larger spatial scales. Classes within the classification were defined using multivariate clustering methods. These produce hierarchal classifications that enable the user to delineate environmental variation at different levels of detail and associated spatial scales. Large biological datasets were used to tune the classification, so that the physically-based classes maximised discrimination of variation in biological composition at various levels of classification detail. Thus, the MEC provides a general classification that is relevant to most groups of marine organisms (fishes, invertebrates and chlorophyll) and to ecologically important abiotic variables (e.g., temperature, nutrients).An overview report describing the MEC is available as a PDF file (External Link). The overview report covers the conceptual basis for the MEC and results of testing the classification: MEC Overview (PDF 2.7 MB)See here for a longer description: https://www.niwa.co.nz/coasts-and-oceans/our-services/marine-environment-classification_Item Page Created: 2018-11-12 22:47 Item Page Last Modified: 2019-07-24 03:58Owner: steinmetzt_NIWAExclusive Economic Zone (EEZ)No data edit dates availableFields: FID,ENTITY,LAYER,ELEVATION,THICKNESS,COLORMEC EEZ 40 classNo data edit dates availableFields: FID,GRP_40,COUNT_MEC EEZ 20 classNo data edit dates availableFields: FID,GRP_20,COUNT_MEC EEZ 10 classNo data edit dates availableFields: FID,GRP_10,COUNT_MEC EEZ 05 classNo data edit dates availableFields: FID,GRP_5,COUNT_CoastlineNo data edit dates availableFields: FID,NZCOAST_ID,SHAPE_LENG

Refer to the current geographies boundaries table for a list of all current geographies and recent updates. This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)). SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations. The SA2 should: form a contiguous cluster of one or more SA1s, excluding exceptions below, allow the release of multivariate statistics with minimal data suppression, capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area, be socially homogeneous and capture a community of interest. It may have, for example: a shared road network, shared community facilities, shared historical or social links, or socio-economic similarity, ​ form a nested hierarchy with statistical output geographies and administrative boundaries. It must: be built from SA1s, either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area. SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns. In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area. To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2. Zero or nominal population SA2s To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include: SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara. SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council. SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name): 400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency. SA2 numbering and naming Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City). SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change. ​ High-definition version This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. ​ Digital data Digital boundary data became freely available on 1 July 2007. ​ Further information To download geographic classifications in table formats such as CSV please use Ariā For more information please refer to the Statistical standard for geographic areas 2023. Contact: geography@stats.govt.nz

Statistical Area 2 2023 update SA2 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure SA2s are relevant and meet criteria before each five-yearly population and dwelling census. SA2 2023 contains 135 new SA2s. Updates were made to reflect real world change ofpopulation and dwelling growthmainly in urban areas, and to make some improvements to their delineation of communities of interest. ​ Description This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2023 as defined by Stats NZ (the custodian), clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This clipped version contains 2,311 SA2 areas. SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations. ​ The SA2 should: form a contiguous cluster of one or more SA1s, excluding exceptions below, allow the release of multivariate statistics with minimal data suppression, capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area, be socially homogeneous and capture a community of interest. It may have, for example: · a shared road network, · shared community facilities, · shared historical or social links, or · socio-economic similarity, form a nested hierarchy with statistical output geographies and administrative boundaries. It must: · be built from SA1s, · either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils. SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents. In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area. SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns. In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area. To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2. ​ Zero or nominal population SA2s To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include: · SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara. · SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas · SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council. · SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name): 400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency. ​ SA2 numbering and naming Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City). SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change. ​ For more information please refer to the Statistical standard for geographic areas 2023. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. Digital data Digital boundary data became freely available on 1 July 2007. ​ To download geographic classifications in table formats such as CSV please use Ariā